Roberto Gutiérrez-Guerra

Design and optimization of thermally coupled extractive distillation sequences

Abstract In this paper, design and optimization procedures are developed for a conventional extractive distillation sequence and a thermally coupled extractive distillation scheme. The proposed methodologies detect the optimal values of the design variables in order to guarantee the minimum energy consumption. It was found that the optimum energy consumption can be related to the minimum total annual operating cost, minimum greenhouse gas emissions, higher thermodynamic efficiencies and good theoretical control properties. The methodologies were applied to the study of the separation of several close boiling point mixtures using the two distillation sequences. The results showed that the energy savings predicted in the complex extractive distillation sequence can be achieved along with reductions in greenhouse gas emissions.

Design and Optimization of Pressure Swing Distillation using a Stochastic Algorithm based in the Boltzmann Distribution

The separation of azeotropic mixtures with help of distillation is an important unit operation in chemical and pharmaceutical industry. The literature focuses on different alternatives for the separation of azeotropic mixtures, such as extractive distillation, azeotropic distillation, heterogeneous azeotropic distillation, vacuum distillation and the Pressure Swing Distillation (PSD). This work describes an approach for PSD in continuous flow for binary azeotropic mixture separation sensible to the pressure changes. The main advantages of this process compared to the others are: i) no additional substances (entrainer) have to be used, and ii) for the continuous flow operation heat integration is possible and it can save energy. The PSD has been optimized with a novel stochastic algorithm called Boltzmann Univariate Marginal Distribution Algorithm (BUMDA). The performance of BUMDA is robust and highly efficient as shown by the experiments conducted (although it does not guarantee optimality). This work makes two specific contributions: 1) the optimization of the PSD in continuous for a binary azeotropic mixture; 2) the application of the BUMDA stochastic algorithm, and the constraint handling technique.

Constrained evolutionary optimization of a distillation train in chemical engineering

The optimal design and synthesis of distillation systems remains one of the most challenging problems in process engineering. The goal of this paper is to introduce an evolutionary approach for the optimization of the total energy consumption of distillation systems with constraints. Moreover, the contribution of this paper is a novel constraint handling technique that manages design goals as equality constraints, such as the purity and the recovery of the final components. In the literature of these problems prevail the use of inequality constraints; although easy to apply they may lead the search to suboptimal solutions. The case study is a distillation column sequence (DCS) for the separation of four components; this problem is easy to describe yet complex to solve so our approach can show its advantages. The evolutionary algorithm Boltzmann Univariate Marginal Distribution Algorithm, (BUMDA), performs the optimization. AspenONE©software is used for the rigorous evaluation of the fitness function of the population. The results show the efficacy performance of the proposed approach reaching near optimal designs in less than 3000 function evaluations.

Study of Performance of a Novel Stochastic Algorithm based on Boltzmann Distribution (BUMDA) coupled with self-adaptive handling constraints technique to optimize Chemical Engineering process

Abstract The optimal design of distillation systems is a highly non-linear, multivariable and multimodal problem. The rigorous model of distillation columns is represented by mass, equilibrium, sum and heat equations called MESH equations and phase equilibrium calculations (Thermodynamic model). Furthermore, it has several local optimums and is subject to several kind constraints such as, design and topology scheme constraints, and achieves targets of purity and recovery for each split component. In this paper, we propose the employment of a novel stochastic algorithm called Boltzmann Univariate Marginal Distribution Algorithm (BUMDA, Valdez, S. I. et al., 2013) coupled with self-adaptive handling constraints technique to optimize a well-known distillation process scheme. The optimization problem consists in minimizing the total reboiler duty in a distillation train to split a mixture made of four components. The BUMDA’s performance is compared with Differential Evolution (DE) due to the fact that this last algorithm is used frequently in the optimization of distillation columns. The results show that the BUMDA algorithm is better than the DE algorithm regarding effort computing, quality solution, and time used to find solution. The BUMDA algorithm is efficient, trusted, easy to use and of general applicability in any chemical engineering process.